IBM researchers use grocery scanner data to speed investigations during early foodborne illness outbreaks
This study shows that big data and analytics can help identify potential sources of contamination.
Scientists at IBM Research – Almaden, San Jose, Calif., discovered that analyzing retail-scanner data from grocery stores against maps of confirmed cases of foodborne illness can speed early investigations. In the study, “From Farm to Fork: How Spatial-Temporal Data can Accelerate Foodborne Illness Investigation in a Global Food Supply Chain," researchers demonstrated that with as few as 10 medical-examination reports of foodborne illness, they can narrow down the investigation to 12 suspected food products in just a few hours.
Researchers created a data-analytics methodology to review spatio-temporal data, including geographic location and possible time of consumption, for hundreds of grocery product categories. Researchers also analyzed each product for shelf life, geographic location of consumption and likelihood of harboring a particular pathogen, then mapped the information to the known location of illness outbreaks. Next, the system ranked all grocery products by likelihood of contamination in a list from which public health officials could test the Top 12 suspected foods for contamination and alert the public accordingly.